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Cooperation and competition among urban agglomerations in environmental efficiency measurement: A cross-efficiency approach

  • 1.

    School of Public Affairs, University of Science and Technology of China, Hefei 230026, China

  • 2.

    School of Economics, Hefei University of Technology, Hefei 230009, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0028
More Information
  • Author Bio:

    Xiaoxing Liang is a PhD candidate of Public Management at the School of Public Affairs, University of Science and Technology of China. His research focuses on urban public management, green development measurement

    Zhixiang Zhou is an Associate Professor at the School of Economics, Hefei University of Technology. His research focuses on efficiency measurement, blockchain in finical application

  • Corresponding author: E-mail: zhixiangzhou@hfut.edu.cn
  • Received Date: 09 February 2022
  • Accepted Date: 08 March 2022
    Abstract

  • Abstract

    Environmental efficiency has become a key indicator in describing the capacity of regional resource utilization with consideration of the negative externality to nature. Notably, with the development of urban agglomerations all over the world, the role and strategy of efficiency measurement for cities should be reorganized to deal with the complex relationships among cities based on urban agglomerations. In this paper, we construct a set of data envelopment analysis (DEA) models based on a peer-evaluation mode with consideration to the cooperative relationships among cities within the same urban agglomeration together with the competitive relationships between different urban agglomerations. Then, this paper we analyze the environmental efficiency of 48 Chinese mainland cities belonging to the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA), Yangtze River Delta Urban Agglomerations (YRDUA), and Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA). This was accomplished during 2014 to 2019 by using four inputs, two desirable outputs, and two undesirable outputs. The results of efficiency scores indicate that the environmental efficiency trend increased during the time series from 2014 to 2019 while the difference on environmental efficiency among different cities and urban agglomerations are significant. The BTHUA is the best performing urban agglomeration with much higher environmental efficiency scores in all the years. Besides, this paper selected 11 influencing factors based on three different angles to analyze the internal and external environments to environmental efficiency scores for providing further inspiration to managers.

    Graphical abstract

      Environmental efficiency results for cities within three Chinese urban agglomerations.

    Abstract

    Environmental efficiency has become a key indicator in describing the capacity of regional resource utilization with consideration of the negative externality to nature. Notably, with the development of urban agglomerations all over the world, the role and strategy of efficiency measurement for cities should be reorganized to deal with the complex relationships among cities based on urban agglomerations. In this paper, we construct a set of data envelopment analysis (DEA) models based on a peer-evaluation mode with consideration to the cooperative relationships among cities within the same urban agglomeration together with the competitive relationships between different urban agglomerations. Then, this paper we analyze the environmental efficiency of 48 Chinese mainland cities belonging to the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA), Yangtze River Delta Urban Agglomerations (YRDUA), and Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA). This was accomplished during 2014 to 2019 by using four inputs, two desirable outputs, and two undesirable outputs. The results of efficiency scores indicate that the environmental efficiency trend increased during the time series from 2014 to 2019 while the difference on environmental efficiency among different cities and urban agglomerations are significant. The BTHUA is the best performing urban agglomeration with much higher environmental efficiency scores in all the years. Besides, this paper selected 11 influencing factors based on three different angles to analyze the internal and external environments to environmental efficiency scores for providing further inspiration to managers.

    • Public Summary

    • This paper analyzes the regional environmental efficiency by using the data of China’s urban agglomerations.
    • This paper considers the cooperation and competition relationship among cities in environmental efficiency measurement.
    • A cross efficiency model is constructed based on the cooperation and competition relationship with considering the performance of undesirable output.

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    • References(34)
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    Sexton T R, Silkman R H, Hogan A J. Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986, 32: 73–105. doi: 10.1002/ev.1441
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    Shi T, Zhang X, Du H, et al. Urban water resource utilization efficiency in China. Chinese Geographical Science, 2015, 25 (6): 684–697. doi: 10.1007/s11769-015-0773-y
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    Song M, An Q, Zhang W, et al. Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 2012, 16 (7): 4465–4469. doi: 10.1016/j.rser.2012.04.052
    [18]
    Song M, Zhang L, Liu W, et al. Bootstrap-DEA analysis of BRICS' energy efficiency based on small sample data. Applied Energy, 2013, 112: 1049–1055. doi: 10.1016/j.apenergy.2013.02.064
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    Song M, Wang S, Liu W. A two-stage DEA approach for environmental efficiency measurement. Environmental Monitoring and Assessment, 2014, 186 (5): 3041–3051. doi: 10.1007/s10661-013-3599-z
    [20]
    Sueyoshi T, Yuan Y, Goto M. A literature study for DEA applied to energy and environment. Energy Economics, 2016, 62: 104–124. doi: 10.1016/j.eneco.2016.11.006
    [21]
    Tsai H, Wu J, Sun J. Cross-efficiency evaluation of Taiwan's international tourist hotels under competitive and cooperative relationships. Journal of China Tourism Research, 2013, 9 (4): 413–428. doi: 10.1080/19388160.2013.841500
    [22]
    Wang L, Zhou Z, Yang Y, et al. Green efficiency evaluation and improvement of Chinese ports: A cross-efficiency model. Transportation Research Part D: Transport and Environment, 2020, 88: 102590. doi: 10.1016/j.trd.2020.102590
    [23]
    Wang Y, Chin K. Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics, 2010, 128 (1): 332–338. doi: 10.1016/j.ijpe.2010.07.032
    [24]
    Wang S, Xing L, Chen H. Impact of marine industrial structure on environmental efficiency. Management of Environmental Quality: An International Journal, 2020, 31 (1): 111–129. doi: 10.1108/MEQ-06-2019-0119
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    Wang K, Zhang X, Wei Y M, et al. Regional allocation of CO2 emissions allowance over provinces in China by 2020. Energy Policy, 2013, 54: 214–229. doi: 10.1016/j.enpol.2012.11.030
    [26]
    Wu J, Zhu Q, Yin P, et al. Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices. Operational Research, 2017, 17 (3): 715–735. doi: 10.1007/s12351-015-0203-z
    [27]
    Xie B C, Shang L F, Yang S B, et al. Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries. Energy, 2014, 74: 147–157. doi: 10.1016/j.energy.2014.04.109
    [28]
    Yang F, Ang S, Xia Q, et al. Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. European Journal of Operational Research, 2012, 223 (2): 483–488. doi: 10.1016/j.ejor.2012.07.001
    [29]
    Yang Z, Xia J, Zou L, et al. Efficiency and driving force assessment of an integrated urban water use and wastewater treatment system: Evidence from spatial panel data of the urban agglomeration on the middle reaches of the Yangtze River. Science of The Total Environment, 2022, 805: 150232. doi: 10.1016/j.scitotenv.2021.150232
    [30]
    Zhang B, Lu D, He Y, et al. The efficiencies of resource-saving and environment: A case study based on Chinese cities. Energy, 2018, 150: 493–507. doi: 10.1016/j.energy.2018.03.004
    [31]
    Zhou P, Ang B W. Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 2008, 36 (8): 2911–2916. doi: 10.1016/j.enpol.2008.03.041
    [32]
    Zhou Z, Guo X, Wu H, et al. Evaluating air quality in China based on daily data: Application of integer data envelopment analysis. Journal of Cleaner Production, 2018, 198: 304–311. doi: 10.1016/j.jclepro.2018.06.180
    [33]
    Zhou Z, Wu H, Song P. Measuring the resource and environmental efficiency of industrial water consumption in China: A non-radial directional distance function. Journal of Cleaner Production, 2019, 240: 118169. doi: 10.1016/j.jclepro.2019.118169
    [34]
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    Figure  1.  Average cross-efficiency scores for three agglomerations.

    Figure  2.  Box plot for all the cities during from 2014 to 2019.

    [1]
    Apergis N, Aye G C, Barros C P, et al. Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics, 2015, 51: 45–53. doi: 10.1016/j.eneco.2015.05.022
    [2]
    Azadeh A, Amalnick M S, Ghaderi S F, et al. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy Policy, 2007, 35 (7): 3792–3806. doi: 10.1016/j.enpol.2007.01.018
    [3]
    Bian Y, Yang F. Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy. Energy Policy, 2010, 38 (4): 1909–1917. doi: 10.1016/j.enpol.2009.11.071
    [4]
    Charnes A, Cooper W W, Rhodes E. Measuring the inefficiency of decision making units. European Journal of Operational Research, 1978, 2 (6): 429–444. doi: 10.1016/0377-2217(78)90138-8
    [5]
    Cheng Y, Shao T, Lai H, et al. Total-factor eco-efficiency and its influencing factors in the Yangtze River delta urban agglomeration, China. International Journal of Environmental Research and Public Health, 2019, 16 (20): 3814. doi: 10.3390/ijerph16203814
    [6]
    Doyle J, Green R. Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 1994, 45 (5): 567–578. doi: 10.1057/jors.1994.84
    [7]
    Geng Z, Dong J, Han Y, et al. Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes. Applied Energy, 2017, 205: 465–476. doi: 10.1016/j.apenergy.2017.07.132
    [8]
    Honma S, Hu J L. Total-factor energy efficiency of regions in Japan. Energy Policy, 2008, 36 (2): 821–833. doi: 10.1016/j.enpol.2007.10.026
    [9]
    Hu J L, Kao C H. Efficient energy-saving targets for APEC economies. Energy Policy, 2007, 35 (1): 373–382. doi: 10.1016/j.enpol.2005.11.032
    [10]
    Liang L, Wu J, Cook W D, et al. The DEA game cross-efficiency model and its Nash equilibrium. Operations Research, 2008, 56 (5): 1278–1288. doi: 10.1287/opre.1070.0487
    [11]
    Liu S, Zhang Z, Wang Y, et al. PM2.5 emission characteristics of coal-fired power plants in Beijing-Tianjin-Hebei region, China. Atmospheric Pollution Research, 2019, 10 (3): 954–959. doi: 10.1016/j.apr.2019.01.003
    [12]
    Long X, Chen B, Park B. Effect of 2008’s Beijing Olympic Games on environmental efficiency of 268 China’s cities. Journal of Cleaner Production, 2018, 172: 1423–1432. doi: 10.1016/j.jclepro.2017.10.209
    [13]
    Özkara Y, Atak M. Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey. Energy, 2015, 93: 495–510. doi: 10.1016/j.energy.2015.09.036
    [14]
    Pan H, Zhang H, Zhang X. China’s provincial industrial energy efficiency and its determinants. Mathematical and Computer Modelling, 2013, 58 (5-6): 1032–1039. doi: 10.1016/j.mcm.2012.09.006
    [15]
    Sexton T R, Silkman R H, Hogan A J. Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986, 32: 73–105. doi: 10.1002/ev.1441
    [16]
    Shi T, Zhang X, Du H, et al. Urban water resource utilization efficiency in China. Chinese Geographical Science, 2015, 25 (6): 684–697. doi: 10.1007/s11769-015-0773-y
    [17]
    Song M, An Q, Zhang W, et al. Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 2012, 16 (7): 4465–4469. doi: 10.1016/j.rser.2012.04.052
    [18]
    Song M, Zhang L, Liu W, et al. Bootstrap-DEA analysis of BRICS' energy efficiency based on small sample data. Applied Energy, 2013, 112: 1049–1055. doi: 10.1016/j.apenergy.2013.02.064
    [19]
    Song M, Wang S, Liu W. A two-stage DEA approach for environmental efficiency measurement. Environmental Monitoring and Assessment, 2014, 186 (5): 3041–3051. doi: 10.1007/s10661-013-3599-z
    [20]
    Sueyoshi T, Yuan Y, Goto M. A literature study for DEA applied to energy and environment. Energy Economics, 2016, 62: 104–124. doi: 10.1016/j.eneco.2016.11.006
    [21]
    Tsai H, Wu J, Sun J. Cross-efficiency evaluation of Taiwan's international tourist hotels under competitive and cooperative relationships. Journal of China Tourism Research, 2013, 9 (4): 413–428. doi: 10.1080/19388160.2013.841500
    [22]
    Wang L, Zhou Z, Yang Y, et al. Green efficiency evaluation and improvement of Chinese ports: A cross-efficiency model. Transportation Research Part D: Transport and Environment, 2020, 88: 102590. doi: 10.1016/j.trd.2020.102590
    [23]
    Wang Y, Chin K. Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics, 2010, 128 (1): 332–338. doi: 10.1016/j.ijpe.2010.07.032
    [24]
    Wang S, Xing L, Chen H. Impact of marine industrial structure on environmental efficiency. Management of Environmental Quality: An International Journal, 2020, 31 (1): 111–129. doi: 10.1108/MEQ-06-2019-0119
    [25]
    Wang K, Zhang X, Wei Y M, et al. Regional allocation of CO2 emissions allowance over provinces in China by 2020. Energy Policy, 2013, 54: 214–229. doi: 10.1016/j.enpol.2012.11.030
    [26]
    Wu J, Zhu Q, Yin P, et al. Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices. Operational Research, 2017, 17 (3): 715–735. doi: 10.1007/s12351-015-0203-z
    [27]
    Xie B C, Shang L F, Yang S B, et al. Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries. Energy, 2014, 74: 147–157. doi: 10.1016/j.energy.2014.04.109
    [28]
    Yang F, Ang S, Xia Q, et al. Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. European Journal of Operational Research, 2012, 223 (2): 483–488. doi: 10.1016/j.ejor.2012.07.001
    [29]
    Yang Z, Xia J, Zou L, et al. Efficiency and driving force assessment of an integrated urban water use and wastewater treatment system: Evidence from spatial panel data of the urban agglomeration on the middle reaches of the Yangtze River. Science of The Total Environment, 2022, 805: 150232. doi: 10.1016/j.scitotenv.2021.150232
    [30]
    Zhang B, Lu D, He Y, et al. The efficiencies of resource-saving and environment: A case study based on Chinese cities. Energy, 2018, 150: 493–507. doi: 10.1016/j.energy.2018.03.004
    [31]
    Zhou P, Ang B W. Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 2008, 36 (8): 2911–2916. doi: 10.1016/j.enpol.2008.03.041
    [32]
    Zhou Z, Guo X, Wu H, et al. Evaluating air quality in China based on daily data: Application of integer data envelopment analysis. Journal of Cleaner Production, 2018, 198: 304–311. doi: 10.1016/j.jclepro.2018.06.180
    [33]
    Zhou Z, Wu H, Song P. Measuring the resource and environmental efficiency of industrial water consumption in China: A non-radial directional distance function. Journal of Cleaner Production, 2019, 240: 118169. doi: 10.1016/j.jclepro.2019.118169
    [34]
    Zhou Z, Chen Y, Song P, et al. China's urban air quality evaluation with streaming data: A DEA window analysis. Science of The Total Environment, 2020, 727: 138213. doi: 10.1016/j.scitotenv.2020.138213
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